bioRxivGenomicsPreprint
Promoter Structural Variants are Drivers of Genome-Wide Differential Expression in Maize
Manisha MunasingheiD, Andrew C. ReadiD, Aimee J SchulziD, Yaniv J BrandvainiD, Nathan M. SpringeriD, Candice N. HirschiD
Abstract
BackgroundStructural variants (SVs) are large insertions or deletions of DNA sequences. While less numerous than single nucleotide polymorphisms, SVs often account for a greater proportion of nucleotide differences between genomes. Their size and frequent association with repetitive sequences has historically hindered their detection, which has limited the ability to associate this variation with molecular and phenotypic trait variation. While some SVs have been linked to observable traits, it remains unclear whether such effects are rare or broadly distributed across the genome. ResultsTo test for genome-wide relationships between SVs and gene expression, we analyzed genome assemblies and transcriptomic data from 10 tissues across 26 diverse maize inbred lines. We identified SVs amongst these lines and examined variants located within the 1kb promoter region upstream of genes. Thousands of genes showed expression differences associated with promoter SVs, often in a tissue-specific manner. One common feature of these SVs was the presence of transposable element sequences. LTR retrotransposons were enriched amongst promoter SVs associated with differential expression and often reduced expression of the nearby gene. Despite widespread expression changes, we found no enrichment for specific biological functions or pathways among affected genes. ConclusionsOur findings indicate that extant TE-mediated promoter SVs play a significant role in shaping gene expression patterns across the maize genome. However, their phenotypic effects appear limited or context-dependent, suggesting that many variants may have minimal impact outside specific developmental stages or environmental conditions.
- organism: plant (other)
- assay: whole-genome sequencing
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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